Evaluation and Comparison of the Ability of Online Available Prediction Programs to Predict True Linear B-cell Epitopes

Costa, Juan Gabriel; Faccendini, Pablo Luis; Sferco, S. J.; Lagier, Claudia M.; Marcipar, Iván Sergio

doi:10.2174/0929866511320060011

Cited by 15 publications

(3 citation statements)

References 35 publications

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“…Previously, Costa et al [] showed that ABCpred to give the best results compared to BepiPred and other online prediction methods used for predicting linear epitopes. The experience in our study was similar and showed a best epitope pair with one amino acid change that is conserved in all 12 genotypes.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Pan-Specific B-Cell Epitopes From Nucleocapsid Protein of Hantaviruses Causing Hantavirus Cardiopulmonary Syndrome

et al. 2017

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Hantaviruses are emerging viral pathogens that causes hantavirus cardiopulmonary syndrome (HCPS) in the Americas, a severe, sometimes fatal, respiratory disease in humans with a case fatality rate of ≥50%. IgM and IgG-based serological detection methods are the most common approaches used for laboratory diagnosis of hantaviruses. Such emerging viral pathogens emphasizes the need for improved rapid diagnostic devices and vaccines incorporating pan-specific epitopes of genotypes. We predicted linear B-cell epitopes for hantaviruses that are specific to genotypes causing HCPS in humans using in silico prediction servers. We modeled the Andes and Sin Nombre hantavirus nucleocapsid protein to locate the identified epitopes. Based on the mean percent prediction probability score, epitope IMASKSVGS/TAEEKLKKKSAF was identified as the best candidate B-cell epitope specific for hantaviruses causing HCPS. Promiscuous epitopes were identified in the C-terminal of the protein. Our study for the first time has reported pan-specific B-cell epitopes for developing immunoassays in the detection of antibodies to hantaviruses causing HCPS. Identification of epitopes with pan-specific recognition of all genotypes causing HCPS could be valuable for the development of immunodiagnositic tools toward pan-detection of hantavirus antibodies in ELISA. J. Cell. Biochem. 118: 2320-2324, 2017. © 2017 Wiley Periodicals, Inc.

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Section: Discussionmentioning

confidence: 99%

Prediction of Pan-Specific B-Cell Epitopes From Nucleocapsid Protein of Hantaviruses Causing Hantavirus Cardiopulmonary Syndrome

et al. 2017

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“…The ABCpred algorithm was shown to have a better predictive performance, as compared with various physicochemical properties, including hydrophilicity ( 15 ), flexibility ( 27 ) and accessibility ( 28 ). In addition, Costa et al ( 29 ) demonstrated that AAPPred and ABCpred obtained the best results in terms of epitope prediction, as compared with other programs, although AAPPred is no longer available. BepiPred is a traditional algorithm developed by Larsen et al ( 14 ) in 2006 using 14 epitope-annotated proteins and a human immunodeficiency virus dataset.…”

Section: Discussionmentioning

confidence: 99%

Computational approach for predicting the conserved B-cell epitopes of hemagglutinin H7 subtype influenza virus

Wang

Sun

et al. 2016

Experimental and Therapeutic Medicine

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An avian-origin influenza H7N9 virus epidemic occurred in China in 2013–2014, in which >422 infected people suffered from pneumonia, respiratory distress syndrome and septic shock. H7N9 viruses belong to the H7 subtype of avian-origin influenza viruses (AIV-H7). Hemagglutinin (HA) is a vital membrane protein of AIV that has an important role in host recognition and infection. The epitopes of HA are significant determinants of the regularity of epidemic and viral mutation and recombination mechanisms. The present study aimed to predict the conserved B-cell epitopes of AIV-H7 HA using a bioinformatics approach, including the three most effective epitope prediction softwares available online: Artificial Neural Network based B-cell Epitope Prediction (ABCpred), B-cell Epitope Prediction (BepiPred) and Linear B-cell Epitope Prediction (LBtope). A total of 24 strains of Euro-Asiatic AIV-H7 that had been associated with a serious poultry pandemic or had infected humans in the past 30 years were selected to identify the conserved regions of HA. Sequences were obtained from the National Center for Biotechnology Information and Global Initiative on Sharing Avian Influenza Data databases. Using a combination of software prediction and sequence comparisons, the conserved epitopes of AIV-H7 were predicted and clarified. A total of five conserved epitopes [amino acids (aa) 37–52, 131–142, 215–234, 465–484 and 487–505] with a suitable length, high antigenicity and minimal variation were predicted and confirmed. Each obtained a score of >0.80 in ABCpred, 60% in LBtope and a level of 0.35 in Bepipred. In addition, a representative amino acid change (glutamine235-to-leucine235) in the HA protein of the 2013 AIV-H7N9 was discovered. The strategy adopted in the present study may have profound implications on the rapid diagnosis and control of infectious disease caused by H7N9 viruses, as well as by other virulent viruses, such as the Ebola virus.

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“…Methods that have been used for the identification of B cell (including IgE) epitopes include enzymatic cleavage, peptide arrays, phage display techniques, peptide microarray-based immunoassay, Nuclear magnetic resonance spectroscopy and X-crystallography [7]. However, these methods are considered expensive and time consuming [8].…”

Section: Introductionmentioning

confidence: 99%

BCIgEPRED—a Dual-Layer Approach for Predicting Linear IgE Epitopes

Vijayakumar

Gautham

2018

Mol Biol

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⎯Allergy is a common health problem worldwide, especially food allergy. Since B cell epitopes that are recognized by the IgE antibodies act as antigenic determinants for allergy, they play a vital role in diagnostics. Hence, knowledge of an IgE binding epitope in a protein is of particular interest for identifying allergenic proteins. Though IgE epitopes may be conformational or linear, identification of the later is useful especially in food allergens that undergo processing or digestion. Very few computational tools are available for the prediction of linear IgE epitopes. Here we report a prediction system that predicts the exact linear IgE epitope. Since our earlier study on linear B-cell epitope prediction demonstrated the effectiveness of using an exact epitope dataset (in contrast to epitope containing region datasets), the dataset in this study uses only experimentally verified exact IgE, IgG, IgM and IgA epitopes. Models for Support Vector Machine (SVM) and Random Forest (RF) were constructed adopting Dipeptide Deviation from the Expected mean (DDE) feature vector. Extensive validation procedures including five-fold cross validation and two different independent dataset tests have been performed to validate the proposed method, which achieved a balanced accuracy ranging from 74 to 78% with area under receiver operator curve greater than 0.8. Performance of the proposed method was observed to be better (accuracy difference of 16-28%) in comparison to the existing available method. The proposed method is developed as a standalone tool that could be used for predicting IgE epitopes as well as to be incorporated into any allergen prediction toolhttps://github.com/brsaran/BCIgePred.

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Evaluation and Comparison of the Ability of Online Available Prediction Programs to Predict True Linear B-cell Epitopes

Cited by 15 publications

References 35 publications

Prediction of Pan-Specific B-Cell Epitopes From Nucleocapsid Protein of Hantaviruses Causing Hantavirus Cardiopulmonary Syndrome

Prediction of Pan-Specific B-Cell Epitopes From Nucleocapsid Protein of Hantaviruses Causing Hantavirus Cardiopulmonary Syndrome

Computational approach for predicting the conserved B-cell epitopes of hemagglutinin H7 subtype influenza virus

BCIgEPRED—a Dual-Layer Approach for Predicting Linear IgE Epitopes

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